RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912976626253824 |
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| author | Sethuraman, Muralikrishnna G. Fekri, Faramarz |
| author_facet | Sethuraman, Muralikrishnna G. Fekri, Faramarz |
| contents | Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world settings. For instance, in genomics, cyclic regulatory networks are common, and measurements are often corrupted by instrumental noise. To address these challenges, we propose RECLAIM, a causal discovery framework that natively handles both cycles and measurement noise. RECLAIM learns the causal graph structure by maximizing the likelihood of the observed measurements via expectation-maximization (EM), using residual normalizing flows for tractable likelihood computation. We consider two measurement models: (i) Gaussian additive noise, and (ii) a linear measurement system with additive Gaussian noise. We provide theoretical consistency guarantees for both the settings. Experiments on synthetic data and real-world protein signaling datasets demonstrate the efficacy of the proposed method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_20585 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RECLAIM: Cyclic Causal Discovery Amid Measurement Noise Sethuraman, Muralikrishnna G. Fekri, Faramarz Machine Learning Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world settings. For instance, in genomics, cyclic regulatory networks are common, and measurements are often corrupted by instrumental noise. To address these challenges, we propose RECLAIM, a causal discovery framework that natively handles both cycles and measurement noise. RECLAIM learns the causal graph structure by maximizing the likelihood of the observed measurements via expectation-maximization (EM), using residual normalizing flows for tractable likelihood computation. We consider two measurement models: (i) Gaussian additive noise, and (ii) a linear measurement system with additive Gaussian noise. We provide theoretical consistency guarantees for both the settings. Experiments on synthetic data and real-world protein signaling datasets demonstrate the efficacy of the proposed method. |
| title | RECLAIM: Cyclic Causal Discovery Amid Measurement Noise |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.20585 |